Optimal¶

Description¶

The dataset was generated using the expert bot from the BabyAI original repository and adapted to the latest version of the environment. The bot is a hard-coded planner, which solves all the tasks optimally.

Dataset Specs¶


Total Steps	7824
Total Episodes	1000
Dataset Observation Space	`Dict('direction': Discrete(4), 'image': Box(0, 255, (7, 7, 3), uint8), 'mission': Text(1, 256, charset= ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''(),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdeeeffghijklmnnoopqrrssttuvwxyzz{}))`
Dataset Action Space	`Discrete(7)`
Algorithm	BabyAI expert bot
Author	Omar G. Younis
Email	omar@farama.org
Code Permalink	https://github.com/Farama-Foundation/minari-dataset-generation-scripts
Minari Version	`0.5.1` (supported)
Download	`minari download minigrid/BabyAI-PutNextS4N1/optimal-v0`

Environment Specs¶

The following table rows correspond to the Gymnasium environment specifications used to generate the dataset. To read more about what each parameter means you can have a look at the Gymnasium documentation https://gymnasium.farama.org/api/registry/#gymnasium.envs.registration.EnvSpec

This environment can be recovered from the Minari dataset as follows:

import minari

dataset = minari.load_dataset('minigrid/BabyAI-PutNextS4N1/optimal-v0')
env  = dataset.recover_environment()


ID	BabyAI-PutNextS4N1-v0
Observation Space	`Dict('direction': Discrete(4), 'image': Box(0, 255, (7, 7, 3), uint8), 'mission': MissionSpace(<function BabyAIMissionSpace._gen_mission at 0x7f51d65c7b80>, None))`
Action Space	`Discrete(7)`
entry_point	`minigrid.envs.babyai:PutNext`
max_episode_steps	None
reward_threshold	None
nondeterministic	`False`
order_enforce	`True`
disable_env_checker	`False`
kwargs	`{'room_size': 4, 'objs_per_room': 1}`
additional_wrappers	`()`
vector_entry_point	`None`

Evaluation Environment Specs¶

This dataset doesn’t contain an eval_env_spec attribute which means that the specs of the environment used for evaluation are the same as the specs of the environment used for creating the dataset. The following calls will return the same environment:

import minari

dataset = minari.load_dataset('minigrid/BabyAI-PutNextS4N1/optimal-v0')
env  = dataset.recover_environment()
eval_env = dataset.recover_environment(eval_env=True)

assert env.spec == eval_env.spec